Multiparametric Photoacoustic Imaging-Based Identification of Aggressive Prostate Cancer

项目摘要 该方案研究了一种新的多参数光声（mpPA）传感前列腺组织异常 和癌症的侵袭性，并将这一概念转化为临床上可翻译的非侵入性检测， 前列腺癌（PCa）的监测工具。前列腺癌（PCa）是最常见的癌症类型之一 也是美国男性癌症相关死亡的第二大原因。筛选和 监测对于在早期发现PCa（此时更容易治疗）和管理癌症至关重要 治疗PSA检测、直肠检查和经直肠超声（TRUS）成像检查是PCa的典型检查流程 筛选在超声（US）或/和磁共振成像（MRI）引导下进行穿刺活检 作为明确的诊断。然而，在US上等回声或几乎不可见的PCa的患病率范围为25 - 25%， 42%，这使得手术更系统化，而不是病变特异性。虽然MRI引导活检 可以提供高分辨率，但是该系统不是实时的，并且由于尺寸和成本而具有有限的可访问性。给定 由于现有诊断方法的局限性，需要开发一种新的成像模式， 对前列腺癌的敏感性和特异性更高，可及性更好。 在这个建议中，我们设想开发一种先进的非侵入性和非电离成像方式， 利用前列腺特异性膜抗原（PSMA）靶向的两个不同和互补的参数， 同时使用造影剂和氧饱和度，以便更好地识别侵袭性PCa。PSMA是一种 显示其在PCa细胞表面上表达的II整合膜蛋白，已知其在PCa细胞表面上显示高表达。 与侵袭性肿瘤而不是惰性肿瘤的亲和力。然而，分子靶向成像的局限性在于， 事实上，癌症特异性抗原并非在所有癌症类型中表达。事实上，PSMA并不表达于 侵袭性前列腺癌细胞系PC-3。除了靶向成像，缺氧被认为是一种替代癌症 基于组织的生理状态的指示器。血氧水平依赖性（BOLD）信息具有 描述临床显著前列腺肿瘤缺氧的能力。我们的成像方法是基于光声 (PA)超声成像是一种将光学对比度的高对比度与超声成像的高穿透力相结合的混合模式。 所提出的mpPA成像执行靶向分子剂对比度和靶向分子剂浓度的同时定量。 氧饱和度以识别前列腺组织异常和癌症侵袭性。这一概念将 与机器人辅助的TRUS系统集成，用于（1）增强检测PCa的特异性和灵敏度，（2） 作为癌症管理工具的非侵入性治疗监测，以及（3）引导具有增强的肿瘤的活检 的特异性我们将开发一种适用于患者的机器人辅助经直肠成像系统，我们将 在犬类模型中验证这个概念。开发的成像平台具有MRI兼容性， 启用互操作交叉验证。

Project Summary This proposal investigates a novel multiparametric photoacoustic (mpPA) sensing of prostate tissue abnormality and cancer aggressiveness and turns this concept into a clinically translatable non-invasive detection and monitoring tool for prostate cancer (PCa). Prostate cancer (PCa) is one of the most common types of cancer and the second leading cause of cancer-related death among men in the United States. Screening and monitoring are critical to both finding PCa in its early stage, when they are easier to treat, and managing cancer treatment. PSA test, rectal exam, and transrectal ultrasound (TRUS) imaging exam are a typical stream of PCa screening. Needle biopsy is performed under ultrasound (US) or/and magnetic resonance image (MRI) guidance as definite diagnosis. However, the prevalence of isoechoic or nearly invisible PCa on US ranges from 25 to 42%, and this makes the procedure more systematic rather than lesion specific. Although MRI-guided biopsy can provide high resolution, the system is not real-time and has limited accessibility due to size and cost. Given the limitations of existing diagnostic methods, there is a need to develop a new imaging modality that provides high sensitivity and specificity on PCa with better accessibility. In this proposal, we envision developing an advanced noninvasive and nonionizing imaging modality that leveraging two distinct and complementary parameters of Prostate-specific membrane antigen (PSMA)-targeted contrast agent and oxygen saturation simultaneously for better identification of aggressive PCa. PSMA is a type II integral membrane protein that shows its expression on the surface of PCa cell, is known for showing high affinity with aggressive rather than indolent tumor. However, the limitation of molecular targeted imaging is the fact that cancer specific antigen is not expressed in all cancer types. In fact, PSMA is not expressed in the aggressive PCa cell line, PC-3. Aside from targeted imaging, hypoxia is considered as an alternative cancer indicator based on physiological state of the tissue. Blood oxygen level-dependent (BOLD) information has an ability to depict clinically significant prostate tumor hypoxia. Our imaging approach is based on photoacoustic (PA) imaging, a hybrid modality combining high contrast of optical contrast with high penetration of US imaging. The proposed mpPA imaging performs simultaneous quantification of targeted molecular agent contrast and oxygen saturation to identify prostate tissue abnormality and cancer aggressiveness. This concept will be integrated with a robot-assisted TRUS system for (1) enhancing specificity and sensitivity in detecting PCa, (2) non-invasive therapeutic monitoring as a cancer management tool, and (3) guiding biopsy with enhanced tumor specificity. We will develop a robot-assisted transrectal imaging system applicable to patients, and we will validate the concept in a canine model. The developed imaging platform possesses an MRI compatibility, which enables interoperative cross-validation.